Tidy and Streamlined Metabolomics Data Workflows


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Documentation for package ‘metamorphr’ version 0.1.1

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calc_neutral_loss Calculate neutral losses from precursor ion mass and fragment ion masses
collapse_max Collapse intensities of technical replicates by calculating their maximum
collapse_mean Collapse intensities of technical replicates by calculating their mean
collapse_median Collapse intensities of technical replicates by calculating their median
collapse_min Collapse intensities of technical replicates by calculating their minimum
create_metadata_skeleton Create a blank metadata skeleton
filter_blank Filter Features based on their occurrence in blank samples
filter_cv Filter Features based on their coefficient of variation
filter_global_mv Filter Features based on the absolute number or fraction of samples it was found in
filter_grouped_mv Group-based feature filtering
filter_msn Filter Features based on occurrence of fragment ions
filter_mz Filter Features based on their mass-to-charge ratios
filter_neutral_loss Filter Features based on occurrence of neutral losses
impute_bpca Impute missing values using Bayesian PCA
impute_global_lowest Impute missing values by replacing them with the lowest observed intensity (global)
impute_knn Impute missing values using nearest neighbor averaging
impute_lls Impute missing values using Local Least Squares (LLS)
impute_lod Impute missing values by replacing them with the Feature 'Limit of Detection'
impute_mean Impute missing values by replacing them with the Feature mean
impute_median Impute missing values by replacing them with the Feature median
impute_min Impute missing values by replacing them with the Feature minimum
impute_nipals Impute missing values using NIPALS PCA
impute_ppca Impute missing values using Probabilistic PCA
impute_rf Impute missing values using random forest
impute_svd Impute missing values using Singular Value Decomposition (SVD)
impute_user_value Impute missing values by replacing them with a user-provided value
join_metadata Join a featuretable and sample metadata
normalize_cyclic_loess Normalize intensities across samples using cyclic LOESS normalization
normalize_factor Normalize intensities across samples using a normalization factor
normalize_median Normalize intensities across samples by dividing by the sample median
normalize_pqn Normalize intensities across samples using a Probabilistic Quotient Normalization (PQN)
normalize_quantile_all Normalize intensities across samples using standard Quantile Normalization
normalize_quantile_batch Normalize intensities across samples using grouped Quantile Normalization with multiple batches
normalize_quantile_group Normalize intensities across samples using grouped Quantile Normalization
normalize_quantile_smooth Normalize intensities across samples using smooth Quantile Normalization (qsmooth)
normalize_ref Normalize intensities across samples using a reference feature
normalize_sum Normalize intensities across samples by dividing by the sample sum
plot_pca Draws a scores or loadings plot or performs calculations necessary to draw them manually
plot_volcano Draws a Volcano Plot or performs calculations necessary to draw one manually
read_featuretable Read a feature table into a tidy tibble
read_mgf Read a MGF file into a tidy tibble
scale_auto Scale intensities of features using autoscale
scale_center Center intensities of features around zero
scale_level Scale intensities of features using level scaling
scale_pareto Scale intensities of features using Pareto scaling
scale_range Scale intensities of features using range scaling
scale_vast Scale intensities of features using vast scaling
scale_vast_grouped Scale intensities of features using grouped vast scaling
summary_featuretable General information about a feature table and sample-wise summary
toy_metaboscape A small toy data set created from a feature table in MetaboScape style
toy_metaboscape_metadata Sample metadata for the fictional dataset 'toy_metaboscape'
toy_mgf A small toy data set containing MSn spectra
transform_log Transforms the intensities by calculating their log
transform_power Transforms the intensities by calculating their _n_th root